A variety of discrete pixel imaging techniques are known and are presently in use. In general, such techniques rely on the collection or acquisition of data representative of each discrete pixel composing an image matrix. Examples of such imaging techniques exist, including optical character recognition, facial feature recognition, corneal scanning, fingerprint recognition, and virtually any other form of digital imaging which involves the processing of an acquired image, including home or personal desktop scanning. Particular examples abound in the medical imaging field where several modalities, such as magnetic resonance, X-ray, ultrasound, and other techniques, are available for producing the data represented by the pixels. Depending upon the particular modality employed, the pixel data is detected and encoded, such as in the form of digital values. The values are linked to particular relative locations of the pixels in the reconstructed image.
The utility of a processed image is often largely dependent upon the degree to which it can be interpreted by users or processed by subsequent automation. For example, in the field of medical diagnostics imaging, MRI, X-ray, and other images are most useful when they can be easily understood and compared by an attending physician or radiologist. Other examples include biometric analysis where the processed image, such as a cornea or fingerprint, is often further processed by software of hardware based matching algorithms. Typically one impediment to interpretation or further processing is the pixel to pixel variation which is attributable to acquisition noise. While acquisition noise is usually random, there may also be additional structured noise as well which may be observed as artifacts in the image. To mitigate the effects of random noise, many forms of noise reduction filters to improve the final image presentation have been proposed.
Moreover, while a number of image processing parameters may control the final image presentation, it is often difficult to determine which of these parameters, or which combination of the parameters, may be adjusted to provide the optimal image presentation. Often, the image processing techniques must be adjusted in accordance with empirical feedback from an operator, such as a physician or technician.
The facility with which a reconstructed discrete pixel image may be interpreted by an observer may rely upon intuitive factors of which the observer may not be consciously aware. For example, in medical imaging, a physician or radiologist may seek specific structures or specific features in an image such as bone, soft tissue or fluids. Such structures may be physically defined in the image by contiguous edges, contrast, texture, and so forth. Other forms of imaging, such as biometric analysis or optical character recognition, require the identification of specific structures or features, such as ridges or lines, which are similarly defined by contiguous edges and so forth.
The presentation of such features often depends heavily upon the particular image processing technique employed for converting the detected values representative of each pixel to modified values used in the final image. The image processing technique employed can therefore greatly affect the ability of an observer or an analytical device to recognize salient features of interest. The technique should carefully maintain recognizable structures of interest, as well as abnormal or unusual structures, while providing adequate textural and contrast information for interpretation of these structures and surrounding background. Ideally the technique will perform these functions in a computationally efficient manner so that processing times, as well as hardware requirements, can be minimized.
Known signal processing systems for enhancing discrete pixel images suffer from certain drawbacks. For example, such systems may not consistently provide comparable image presentations in which salient features or structures may be easily visualized. Differences in the reconstructed images may result from particularities of individual scanners and circuitry, as well as from variations in the detected parameters (e.g. molecular excitation or received radiation). Differences can also result from the size, composition, position, or orientation of a subject or item being scanned. Signal processing techniques employed in known systems are often difficult to reconfigure or adjust, owing to the relative inflexibility of hardware or firmware devices in which they are implemented or to the coding approach employed in software.
Moreover, certain known techniques for image enhancement may offer excellent results for certain systems, but may not be as suitable for others. For example, in medical imaging, low, medium and high field MRI systems may require substantially different data processing due to the different nature of the data defining the resulting images. In current techniques completely different image enhancement frameworks are employed in such cases. In addition, current techniques may result in highlighting of small, isolated areas which are not important for interpretation and may be distracting to the viewer. Conversely, in techniques enhancing images by feature structure recognition, breaks or discontinuities may be created between separate structural portions, such as along edges. Such techniques may provide some degree of smoothing or edge enhancement, but may not provide satisfactory retention of textures at ends of edges or lines.
Finally, known signal processing techniques often employ computational noise reduction algorithms which are not particularly efficient, resulting in delays in formulation of the reconstituted image or under-utilization of signal processing capabilities. More computationally efficient algorithms would allow both quicker image display, perhaps even approaching the level of real time display for some modalities. Further, more computationally efficient noise reduction algorithms might reduce or eliminate hardware based noise reduction, making them more suitable for diagnostic imaging systems due to both the increased speed and the less stringent equipment requirements.
There is a need, therefore, for a more computationally efficient technique for enhancing discrete pixel images which addresses these concerns. Ideally such a technique would be robust in its implementation, allowing it to be used with any number of pixel imaging modalities with few, if any, modifications.